
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "9"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 92 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 88 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.271099	0.408177	10.129449	1.565987	4.759000	1.766208	9.638597	1.918186	0.7435	0.7454	0.2768	10.328034	10.545673
2459593	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.802657	3.118598	9.217535	2.214347	4.393491	2.294962	4.269784	0.738022	0.7060	0.6988	0.2633	4.371562	4.259797
2459592	dish_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.113794	-0.408720	2.361957	-0.291137	0.191894	0.193823	-1.829878	-0.028505	0.0290	0.0299	0.0023	nan	nan
2459591	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.829506	3.541229	11.398287	3.183940	2.810159	1.295670	1.101864	-0.826274	0.6468	0.6608	0.3765	3.624918	3.954881
2459590	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.059478	3.681769	10.994299	2.243647	2.491123	2.361424	0.670809	-0.445866	0.6492	0.6600	0.3714	4.269852	4.264830
2459589	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.737374	3.250593	10.145260	1.566950	5.676253	1.581898	6.405083	2.257849	0.6665	0.6688	0.3139	3.640588	3.843025
2459588	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.738518	3.167680	10.364522	3.754867	8.991520	2.460590	6.666692	1.910178	0.7552	0.7350	0.2304	6.721668	5.474048
2459587	dish_ok	100.00%	-	-	-	100.00%	0.00%	354.093408	353.690421	inf	inf	111.992214	108.121779	516.674499	594.948225	nan	nan	nan	0.000000	0.000000
2459586	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.529710	1.495232	0.685286	0.047455	0.454480	-0.037039	1.190838	-0.484113	0.6446	0.6552	0.3664	3.574097	3.513161
2459585	dish_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.432829	0.032976	1.854643	-0.329892	0.807067	-0.686358	-0.455803	-0.387714	0.0338	0.0316	0.0019	0.000000	0.000000
2459584	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	19.620945	19.750783	5.723496	7.294068	3.346749	12.187376	0.656105	0.256449	0.6044	0.6205	0.3703	0.000000	0.000000
2459583	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.324572	2.866812	2.414267	2.258979	2.116866	1.823119	0.667688	0.325292	0.6383	0.6495	0.3929	2.996903	2.957884
2459582	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.457700	3.013438	2.073824	1.990871	1.504372	2.166350	1.243844	1.393692	0.6484	0.6585	0.3886	3.914016	3.731414
2459581	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.100170	4.478787	3.029896	2.273919	1.337402	2.232789	0.487574	-0.022362	0.6401	0.6539	0.3969	3.143689	3.156770
2459580	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.456478	3.096660	2.447215	2.462819	1.747839	2.574179	0.479748	0.392321	0.6544	0.6664	0.3849	4.419899	4.059331
2459579	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.524821	3.072479	3.070616	2.801002	1.736820	2.578699	0.840770	0.813052	0.6521	0.6642	0.3867	3.771266	3.932263
2459578	dish_ok	0.00%	100.00%	100.00%	0.00%	-	-	-1.138119	0.941384	0.306567	0.552030	0.287321	-0.182783	0.235173	-0.579128	0.0303	0.0335	0.0034	nan	nan
2459577	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	9.624161	23.074249	1.991744	3.080526	7.230886	10.541040	15.952312	23.127872	0.6420	0.6424	0.3705	3.951111	3.692881
2459576	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.449051	2.730420	2.401411	1.087506	1.385690	0.688240	0.522638	0.156899	0.6701	0.6810	0.3847	0.000000	0.000000
2459575	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.984093	2.676374	1.633742	1.348461	1.087321	0.578897	2.086011	1.067634	0.7241	0.7249	0.3516	0.000000	0.000000
2459574	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.101240	2.828505	1.936198	1.008285	1.942873	0.630173	1.780692	-0.953867	0.6729	0.6858	0.3740	3.746209	3.348399
2459573	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.216904	3.137930	3.091008	3.252840	1.044177	1.509759	0.480410	-0.775295	0.6533	0.6662	0.3970	0.000000	0.000000
2459572	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.022978	2.827261	1.902565	1.207693	0.881518	1.118176	1.265043	0.657139	0.6567	0.6684	0.3967	4.311342	3.357764
2459571	dish_ok	0.00%	100.00%	100.00%	0.00%	-	-	-1.068146	0.855278	0.175786	0.402850	0.859219	-0.997846	0.166366	-0.671850	0.0296	0.0363	0.0027	nan	nan
2459570	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459569	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.047790	3.047807	2.540945	1.465883	2.977259	0.962966	2.959478	1.147231	0.7224	0.7285	0.2683	3.144576	3.025561
2459566	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.557055	3.062010	2.638964	2.109346	1.284970	0.770920	0.944919	-1.056875	0.6588	0.6708	0.4009	8.335353	1.386516
2459565	dish_ok	0.00%	-	-	-	100.00%	0.00%	-2.569066	-2.569066	-2.410329	-2.410329	0.475242	0.475242	0.005642	0.005642	nan	nan	nan	0.000000	0.000000
2459564	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.502422	3.751235	-0.156409	2.621271	-0.749630	0.002746	0.108101	-0.477929	0.6721	0.6643	0.3930	3.066847	2.684292
2459563	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.375607	8.156981	18.212059	23.739625	11.274057	15.902131	9.056070	7.688903	0.6524	0.6407	0.3992	0.000000	0.000000
2459562	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.733619	2.601644	-0.500275	0.880320	-1.612055	-0.528146	-0.111707	-0.055210	0.6668	0.6588	0.3848	3.666381	2.900497
2459561	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.400956	3.024775	0.102717	1.450245	-0.887590	0.082967	-0.460690	-0.764023	0.6674	0.6625	0.3841	1.888745	1.818057
2459560	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.867668	2.685041	-0.323036	1.253826	-0.659501	0.563317	-0.052808	-0.000977	0.6650	0.6560	0.3959	4.365294	3.304324
2459559	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.654360	3.687092	0.160652	1.739355	-0.293030	1.037674	-0.349286	-0.445352	0.6659	0.6634	0.3903	2.921666	2.587372
2459558	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.329152	7.839314	9.780537	12.297936	10.512347	14.753330	-0.972454	-1.870641	0.6656	0.6554	0.4023	2.724315	2.555052
2459557	dish_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0360	0.0303	0.0010	nan	nan
2459556	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.528530	8.222722	10.060076	12.657791	9.337372	13.212654	-2.104185	-3.329560	0.6732	0.6666	0.3877	2.875497	2.733093
2459554	dish_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6633	0.6552	0.4066	nan	nan
2459553	dish_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6689	0.6629	0.3990	nan	nan
2459552	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.988303	9.410009	12.906868	17.510314	13.100720	20.472481	0.546120	0.249780	0.6690	0.6597	0.4069	8.004614	0.000000
2459551	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.208772	2.713938	-0.206246	1.511627	-1.557200	0.443519	-1.361403	-2.108821	0.6588	0.6552	0.4012	3.007037	2.719259
2459550	dish_ok	-	98.35%	98.35%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0371	0.0378	0.0022	nan	nan
2459549	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.277649	7.549208	9.655876	12.886363	10.061620	14.663749	-1.104434	-2.527636	0.6589	0.6561	0.4113	2.825877	2.727444
2459542	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.670018	7.776602	19.123245	25.688623	0.033221	1.122230	-1.230844	-2.188514	0.7247	0.7080	0.3786	4.508431	3.851035
2459541	dish_ok	100.00%	1.34%	1.34%	0.00%	100.00%	0.00%	5.212721	6.662926	11.673933	15.122354	7.344400	11.688015	3.314583	3.727805	0.7131	0.6925	0.3526	3.127501	3.162994
2459540	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.719026	4.301619	1.492261	2.719833	-0.029876	1.833077	-0.188359	0.244417	0.7209	0.6867	0.3780	3.010482	2.551272
2459536	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.394472	11.829017	15.428447	21.151927	13.961279	20.708101	-1.519363	-4.171702	0.7002	0.6974	0.4166	3.845627	3.238689
2459535	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.097007	2.915414	1.051897	2.320207	-2.883212	1.390696	-1.509801	-1.507704	0.7581	0.7178	0.3985	2.759175	2.604569
2459534	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.453915	2.679859	0.237384	1.464517	-1.849531	0.336889	-0.386878	-1.581699	0.7450	0.7188	0.3823	2.913787	2.890364
2459533	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.827907	8.430733	10.154976	13.101408	8.610818	13.162495	-1.383533	-2.515403	0.6881	0.6785	0.4059	2.910707	2.864848
2459532	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.913414	9.783966	13.166564	16.773443	9.850913	16.474424	-1.197941	-2.337909	0.6887	0.6765	0.4079	3.216952	3.167294
2459530	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.073892	2.733970	-0.034636	1.253419	-1.520914	0.505590	-1.315488	-1.456816	0.7122	0.6961	0.4073	3.033029	2.875459
2459527	dish_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459523	dish_ok	100.00%	-	-	-	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459522	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459513	dish_ok	100.00%	100.00%	100.00%	0.00%	-	-	3.894476	4.921949	4.022481	9.113417	2.315618	-0.065782	0.167997	-0.790197	0.0383	0.0328	0.0014	nan	nan
2459508	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.808402	8.810798	8.798079	10.676553	9.096756	10.350166	8.189305	12.281454	0.8660	0.8690	0.3020	0.000000	0.000000
2459505	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.500730	6.195966	10.636153	12.394236	2.817012	6.553339	-0.865072	-0.486918	0.8669	0.8145	0.2508	6.758732	6.112065

Antenna Season Report Notebook¶

Antenna 9 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 9, Node 2:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 9: 2459616

Antenna 9: 2459615

Antenna 9: 2459614

Antenna 9: 2459613

Antenna 9: 2459612

Antenna 9: 2459611

Antenna 9: 2459610

Antenna 9: 2459609

Antenna 9: 2459608

Antenna 9: 2459607

Antenna 9: 2459605

Antenna 9: 2459604

Antenna 9: 2459603

Antenna 9: 2459602

Antenna 9: 2459598

Antenna 9: 2459597

Antenna 9: 2459596

Antenna 9: 2459595

Antenna 9: 2459594

Antenna 9: 2459593

Antenna 9: 2459592

Antenna 9: 2459591

Antenna 9: 2459590

Antenna 9: 2459589

Antenna 9: 2459588

Antenna 9: 2459587

Antenna 9: 2459586

Antenna 9: 2459585

Antenna 9: 2459584

Antenna 9: 2459583

Antenna 9: 2459582

Antenna 9: 2459581

Antenna 9: 2459580

Antenna 9: 2459579

Antenna 9: 2459578

Antenna 9: 2459577

Antenna 9: 2459576

Antenna 9: 2459575

Antenna 9: 2459574

Antenna 9: 2459573

Antenna 9: 2459572

Antenna 9: 2459571

Antenna 9: 2459570

Antenna 9: 2459569

Antenna 9: 2459566

Antenna 9: 2459565

Antenna 9: 2459564

Antenna 9: 2459563

Antenna 9: 2459562

Antenna 9: 2459561

Antenna 9: 2459560

Antenna 9: 2459559

Antenna 9: 2459558

Antenna 9: 2459556

Antenna 9: 2459552

Antenna 9: 2459551

Antenna 9: 2459549

Antenna 9: 2459542

Antenna 9: 2459541

Antenna 9: 2459540

Antenna 9: 2459536

Antenna 9: 2459535

Antenna 9: 2459534

Antenna 9: 2459533

Antenna 9: 2459532

Antenna 9: 2459530

Antenna 9: 2459527

Antenna 9: 2459524

Antenna 9: 2459523

Antenna 9: 2459522

Antenna 9: 2459513

Antenna 9: 2459508

Antenna 9: 2459506

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶